{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 导入模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-08-19 14:26:24,734-INFO: generated new fontManager\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import gzip\n",
    "import numpy as np\n",
    "import random\n",
    "import paddle\n",
    "import paddle.fluid as fluid\n",
    "from paddle.fluid.dygraph import Linear\n",
    "from paddle.fluid.dygraph.nn import Conv2D, Pool2D, Linear\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 准备数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 从飞桨读入数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Cache file /root/.cache/paddle/dataset/mnist/train-images-idx3-ubyte.gz not found, downloading https://dataset.bj.bcebos.com/mnist/train-images-idx3-ubyte.gz \n",
      "Begin to download\n",
      "\n",
      "Download finished\n",
      "Cache file /root/.cache/paddle/dataset/mnist/train-labels-idx1-ubyte.gz not found, downloading https://dataset.bj.bcebos.com/mnist/train-labels-idx1-ubyte.gz \n",
      "Begin to download\n",
      "........\n",
      "Download finished\n"
     ]
    }
   ],
   "source": [
    "# 如果～/.cache/paddle/dataset/mnist/目录下没有MNIST数据，API会自动将MINST数据下载到该文件夹下\n",
    "# 设置数据读取器，读取MNIST数据训练集\n",
    "trainset = paddle.dataset.mnist.train()\n",
    "# 包装数据读取器，每次读取的数据数量设置为batch_size=100\n",
    "train_reader = paddle.batch(trainset, batch_size=100)\n",
    "# 将读入的数据打乱顺序\n",
    "train_reader = fluid.io.shuffle(train_reader, 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 将数据集从List类型转换为numpy数组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "图像数据形状和对应数据为: (100, 1, 28, 28)\n",
      "图像标签形状和对应数据为: (100, 1) [7.]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 以迭代的形式读取数据\n",
    "for batch_id, data in enumerate(train_reader()):\n",
    "    # 获得图像数据，并转为float32类型的数组，遍历list，将每个元素从728转换为[1,28,28]，最后将list转换为array\n",
    "    img_data = np.array([np.reshape(np.array(x[0]).astype('float32'), [1, 28, 28]) for x in data])\n",
    "    # 获得图像标签数据，并转为float32类型的数组，将list转换为array并在后面添加一个维度\n",
    "    label_data = np.array([x[1] for x in data]).astype('float32').reshape(-1, 1)\n",
    "    # 打印数据形状\n",
    "    print(\"图像数据形状和对应数据为:\", img_data.shape)\n",
    "#     print(\"图像数据形状和对应数据为:\", img_data.shape, img_data[0])\n",
    "    print(\"图像标签形状和对应数据为:\", label_data.shape, label_data[0])\n",
    "    break\n",
    "# 显示第一batch的第一个图像\n",
    "img = img_data[0][0]\n",
    "plt.figure(\"Image\")  # 图像窗口名称\n",
    "plt.imshow(img)\n",
    "plt.axis('on')  # 关掉坐标轴为 off\n",
    "plt.title('image')  # 图像题目\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 定义网络"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 定义单层全连接网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MNIST(fluid.dygraph.Layer):\n",
    "    def __init__(self):\n",
    "        super(MNIST, self).__init__()\n",
    "#         输入长度是28*28=784，输出则是判断的数字类别\n",
    "        self.fc = Linear(input_dim=784, output_dim=1, act=None)\n",
    "\n",
    "    def forward(self, inputs):\n",
    "        inputs = fluid.layers.reshape(inputs, (-1, 784))\n",
    "        outputs = self.fc(inputs)\n",
    "        return outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 定义多层全连接网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MNIST(fluid.dygraph.Layer):\n",
    "    def __init__(self):\n",
    "        super(MNIST, self).__init__()\n",
    "        # 定义两层全连接隐含层，输出维度是10，激活函数为sigmoid\n",
    "        self.fc1 = Linear(input_dim=784, output_dim=10, act='sigmoid') # 隐含层节点为10，可根据任务调整\n",
    "        self.fc2 = Linear(input_dim=10, output_dim=10, act='sigmoid')\n",
    "        # 定义一层全连接输出层，输出维度是1，不使用激活函数\n",
    "        self.fc3 = Linear(input_dim=10, output_dim=1, act=None)\n",
    "    \n",
    "    # 定义网络的前向计算\n",
    "    def forward(self, inputs, label=None):\n",
    "        inputs = fluid.layers.reshape(inputs, [inputs.shape[0], 784])\n",
    "        outputs1 = self.fc1(inputs)\n",
    "        outputs2 = self.fc2(outputs1)\n",
    "        outputs_final = self.fc3(outputs2)\n",
    "        return outputs_final"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 多层卷积神经网络实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 多层卷积神经网络实现\n",
    "class MNIST(fluid.dygraph.Layer):\n",
    "     def __init__(self):\n",
    "         super(MNIST, self).__init__()\n",
    "         \n",
    "         # 定义卷积层，输出特征通道num_filters设置为20，卷积核的大小filter_size为5，卷积步长stride=1，padding=2\n",
    "         # 激活函数使用relu\n",
    "         self.conv1 = Conv2D(num_channels=1, num_filters=20, filter_size=5, stride=1, padding=2, act='relu')\n",
    "         # 定义池化层，池化核pool_size=2，池化步长为2，选择最大池化方式\n",
    "         self.pool1 = Pool2D(pool_size=2, pool_stride=2, pool_type='max')\n",
    "         # 定义卷积层，输出特征通道num_filters设置为20，卷积核的大小filter_size为5，卷积步长stride=1，padding=2\n",
    "         self.conv2 = Conv2D(num_channels=20, num_filters=20, filter_size=5, stride=1, padding=2, act='relu')\n",
    "         # 定义池化层，池化核pool_size=2，池化步长为2，选择最大池化方式\n",
    "         self.pool2 = Pool2D(pool_size=2, pool_stride=2, pool_type='max')\n",
    "         # 定义一层全连接层，输出维度是1，不使用激活函数\n",
    "         self.fc = Linear(input_dim=980, output_dim=1, act=None)\n",
    "         \n",
    "    # 定义网络前向计算过程，卷积后紧接着使用池化层，最后使用全连接层计算最终输出\n",
    "     def forward(self, inputs):\n",
    "         x = self.conv1(inputs)\n",
    "         x = self.pool1(x)\n",
    "         x = self.conv2(x)\n",
    "         x = self.pool2(x)\n",
    "         x = fluid.layers.reshape(x, [x.shape[0], -1])\n",
    "         x = self.fc(x)\n",
    "         return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 训练网络，并保存模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0, batch: 0, loss is: [17.600958]\n",
      "epoch: 0, batch: 200, loss is: [2.51957]\n",
      "epoch: 0, batch: 400, loss is: [1.8720033]\n",
      "epoch: 1, batch: 0, loss is: [1.5634558]\n",
      "epoch: 1, batch: 200, loss is: [1.6354859]\n",
      "epoch: 1, batch: 400, loss is: [1.7413342]\n",
      "epoch: 2, batch: 0, loss is: [1.1738201]\n",
      "epoch: 2, batch: 200, loss is: [1.5190034]\n",
      "epoch: 2, batch: 400, loss is: [2.9117508]\n",
      "epoch: 3, batch: 0, loss is: [1.3247323]\n",
      "epoch: 3, batch: 200, loss is: [1.2803106]\n",
      "epoch: 3, batch: 400, loss is: [2.7125998]\n",
      "epoch: 4, batch: 0, loss is: [1.1603993]\n",
      "epoch: 4, batch: 200, loss is: [1.525015]\n",
      "epoch: 4, batch: 400, loss is: [1.3977467]\n",
      "epoch: 5, batch: 0, loss is: [1.1423038]\n",
      "epoch: 5, batch: 200, loss is: [1.0669457]\n",
      "epoch: 5, batch: 400, loss is: [1.347811]\n",
      "epoch: 6, batch: 0, loss is: [0.9278978]\n",
      "epoch: 6, batch: 200, loss is: [1.9005439]\n",
      "epoch: 6, batch: 400, loss is: [1.430917]\n",
      "epoch: 7, batch: 0, loss is: [1.4109778]\n",
      "epoch: 7, batch: 200, loss is: [1.5345002]\n",
      "epoch: 7, batch: 400, loss is: [1.8445978]\n",
      "epoch: 8, batch: 0, loss is: [1.5167334]\n",
      "epoch: 8, batch: 200, loss is: [0.99144524]\n",
      "epoch: 8, batch: 400, loss is: [1.5990667]\n",
      "epoch: 9, batch: 0, loss is: [1.2438427]\n",
      "epoch: 9, batch: 200, loss is: [1.5073814]\n",
      "epoch: 9, batch: 400, loss is: [1.5736461]\n"
     ]
    }
   ],
   "source": [
    "# 读取数据集数据，并乱序后放入模型进行训练\n",
    "trainset = paddle.dataset.mnist.train()\n",
    "train_reader = paddle.batch(trainset, batch_size=100)\n",
    "train_reader = fluid.io.shuffle(train_reader, 100)\n",
    "\n",
    "use_gpu = False # 如果使用Gpu进行训练则修改为True\n",
    "place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()\n",
    "with fluid.dygraph.guard(place):\n",
    "    model = MNIST()\n",
    "    model.train()\n",
    "\n",
    "    # 四种优化算法的设置方案，可以逐一尝试效果\n",
    "    optimizer = fluid.optimizer.SGDOptimizer(learning_rate=0.001, parameter_list=model.parameters())\n",
    "    # optimizer = fluid.optimizer.MomentumOptimizer(learning_rate=0.01, momentum=0.9, parameter_list=model.parameters())\n",
    "    # optimizer = fluid.optimizer.AdagradOptimizer(learning_rate=0.01, parameter_list=model.parameters())\n",
    "    # optimizer = fluid.optimizer.AdamOptimizer(learning_rate=0.01, parameter_list=model.parameters())\n",
    "\n",
    "    EPOCH_NUM = 10\n",
    "    for epoch_id in range(EPOCH_NUM):\n",
    "        for batch_id, data in enumerate(train_reader()):\n",
    "            # 获得图像数据，并转为float32类型的数组，遍历list，将每个元素从728转换为[1,28,28]，最后将list转换为array\n",
    "            image_data = np.array([np.reshape(np.array(x[0]).astype('float32'), [1, 28, 28]) for x in data])\n",
    "            # 获得图像标签数据，并转为float32类型的数组，将list转换为array并在后面添加一个维度\n",
    "            label_data = np.array([x[1] for x in data]).astype('float32').reshape(-1, 1)\n",
    "            # print(image_data.shape)\n",
    "            image = fluid.dygraph.to_variable(image_data)\n",
    "            label = fluid.dygraph.to_variable(label_data)\n",
    "\n",
    "            # 前向计算的过程\n",
    "            predict = model(image)\n",
    "\n",
    "            # 计算损失，取一个批次样本损失的平均值\n",
    "            loss = fluid.layers.square_error_cost(predict, label)\n",
    "            avg_loss = fluid.layers.mean(loss)\n",
    "\n",
    "            # 计算损失，使用交叉熵损失函数，取一个批次样本损失的平均值\n",
    "#             loss = fluid.layers.cross_entropy(predict, label)\n",
    "#             avg_loss = fluid.layers.mean(loss)\n",
    "\n",
    "            # 每训练了200批次的数据，打印下当前Loss的情况\n",
    "            if batch_id % 200 == 0:\n",
    "                print(\"epoch: {}, batch: {}, loss is: {}\".format(epoch_id, batch_id, avg_loss.numpy()))\n",
    "\n",
    "            # 后向传播，更新参数的过程\n",
    "            avg_loss.backward()\n",
    "            optimizer.minimize(avg_loss)\n",
    "            model.clear_gradients()\n",
    "\n",
    "    # 保存模型参数\n",
    "    fluid.save_dygraph(model.state_dict(), 'mnist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[name conv2d_0.w_0, dtype: VarType.FP32 shape: [20, 1, 5, 5] \tlod: {}\n",
      "\tdim: 20, 1, 5, 5\n",
      "\tlayout: NCHW\n",
      "\tdtype: float\n",
      "\tdata: [-0.227246 0.0548281 0.251354 -0.0798577 0.306433 -0.0788795 -0.0172948 0.762193 0.483228 -0.15396 0.0135982 0.0169954 -0.253063 -0.330747 -0.249401 0.247155 -0.153549 -0.115179 0.0774885 0.0665917 -0.37785 0.423157 -0.217491 -0.450712 -0.0188339 -0.0230892 -0.538814 -0.178613 0.328706 0.387427 -0.423183 0.323581 0.206155 -0.04375 -0.10055 0.765898 0.264762 0.109597 0.0377134 -0.0279006 0.248949 0.176892 -0.126463 -0.35219 -0.288167 -0.239866 -0.509895 0.22303 0.13139 0.306384 -0.106253 -0.0144631 0.474492 -0.406996 -0.0548578 -0.243999 -0.614443 0.23912 -0.0840249 0.300494 -0.336129 0.104439 -0.123694 -0.0852219 -0.316131 0.286032 0.152325 0.275713 -0.0192928 -0.00882145 0.0805475 0.185121 -0.031738 0.385909 -0.0583669 -0.370359 0.0306013 0.169645 -0.124752 -0.0764654 0.27568 0.107534 0.426383 0.411237 -0.310461 0.558313 -0.246247 0.104172 0.231704 0.547704 0.139176 -0.109574 -0.28206 0.507848 0.148933 0.445762 0.183472 -0.204767 0.0534182 0.150497 -0.125364 -0.329501 0.307435 -0.340437 0.111094 0.563773 0.543794 -0.262871 0.345355 0.218346 -0.238018 0.400431 -0.525994 0.284732 -0.428919 -0.0225804 -0.0389754 0.0843101 0.307159 -0.0614498 -0.106985 -0.454815 0.0259965 -0.0973571 -0.448972 0.351365 -0.237744 0.259454 -0.236094 -0.327421 -0.125579 -0.201656 -0.0127986 0.272579 0.0493162 0.22211 0.211635 0.115002 -0.210376 0.0103721 0.217024 -0.306399 0.00227449 -0.318282 -0.116029 0.0843067 -0.354173 -0.0325888 -0.311984 0.188242 -0.00427163 0.762142 -0.370043 0.122121 0.0927956 -0.294816 -0.322087 -0.11884 0.344933 -0.116072 0.0783905 0.193949 0.0580444 -0.282808 0.071817 0.441748 0.462784 0.00808839 -0.0394948 0.183692 0.120289 0.233904 0.171621 0.0270731 0.102981 0.188186 -0.155376 0.435382 -0.0580168 0.672496 0.00579504 -0.748377 0.170818 0.0129802 0.393468 0.0386617 -0.0350363 0.435921 -0.787254 -0.200266 0.475196 0.0166195 -0.199721 -0.361987 0.11512 0.387212 0.22304 -0.0283572 0.117841 -0.0646937 0.0138029 0.0761266 0.12634 -0.0611536 -0.32761 0.134049 -0.124544 -0.508289 -0.133324 -0.0582774 -0.258735 -0.373043 -0.0786714 -0.190513 -0.151368 0.121561 0.00347429 -0.207556 -0.246281 -0.381161 0.0561858 -0.163524 0.111459 0.038698 0.501631 0.110227 -0.0348623 0.0351762 0.48639 -0.0195005 0.0889384 -0.0632614 -0.202006 0.243804 -0.0756823 -0.333895 0.137718 0.469302 -0.441927 -0.0967047 0.382477 0.117824 -0.0237536 -0.218132 0.0862621 0.251271 -0.509126 -0.280272 0.134147 -0.272376 -0.0349768 -0.165535 0.267103 0.193803 -0.188984 0.333985 0.168576 -0.105126 0.195761 -0.111581 -0.318858 0.298917 -0.260599 -0.319298 0.357294 -0.418496 0.122133 0.524157 -0.123918 -0.353405 -0.168329 -0.0838268 0.402537 -0.0152255 -0.125446 -0.0584535 0.0477349 -0.0401909 -0.0377567 -0.265219 0.200875 0.0183878 0.352704 -0.22053 -0.429081 0.0426243 -0.0863928 -0.472136 0.598245 -0.00416186 0.0282666 0.282926 0.952686 0.0768105 0.498454 -0.0895823 0.242195 -0.187837 -0.325324 0.451703 -0.207083 0.330258 0.134639 -0.302738 -0.414033 -0.0854899 -0.081449 -0.00703471 0.275525 -0.111829 0.122421 -0.215591 0.213164 0.386205 0.18175 -0.143683 -0.293039 -0.561904 0.404913 -0.241563 0.33923 0.106055 0.0879772 0.0915635 0.0968055 0.0274511 0.205555 0.38516 -0.500758 -0.327258 0.344763 0.056876 0.181277 -0.469355 -0.173191 -0.125399 -0.22511 0.0217277 0.203111 -0.00624135 0.238832 0.221938 -0.17754 0.257443 -0.0116583 0.262424 0.139388 -0.064624 0.222324 0.0493808 -0.106288 -0.17219 -0.0556469 -0.226814 0.437798 -0.246132 0.230523 -0.207435 -0.137444 -0.163695 0.365348 0.00666816 0.378514 0.144067 0.297044 -0.529493 0.10494 -0.278285 0.0633915 -0.0802393 -0.0875525 0.535541 -0.0608996 0.361904 -0.584338 0.123749 -0.0850199 -0.0997997 -0.0708689 0.129586 -0.131185 0.113403 -0.334728 0.112043 0.166204 -0.397651 0.0276545 -0.0524479 0.242461 -0.111372 -0.235561 -0.0914527 0.284368 -0.16511 0.159017 0.0718177 -0.211697 0.607009 -0.232372 0.146451 -0.0536999 0.131027 0.179055 -0.531251 -0.417233 -0.554783 -0.275025 -0.00420721 -0.221429 0.354873 0.0116216 0.145484 -0.26723 0.520727 -0.258365 -0.217652 -0.0323385 0.382681 -0.577586 -0.334269 0.39822 -0.140551 -0.488885 -0.0535144 -0.0433972 -0.385511 0.695686 -0.618814 -0.0282541 -0.107873 -0.281153 -0.168272 -0.132517 -0.044689 0.174949 0.108602 -0.219675 -0.0165635 0.581316 0.0421807 0.729517 -0.290452 -0.0552962 0.336046 0.0412659 -0.368479 -0.147771 0.438988 -0.132097 -0.135937 0.121798 -0.454744 -0.470019 -0.28179 -0.103352 0.277309 -0.477825 0.118541 0.00446854 -0.268796 0.103049 0.383281 0.254466 -0.142113 -0.311242 -0.00256443 0.636356 -0.203472 -0.332393 -0.185991 0.150005 0.272706 0.243614 -0.323861 -0.314608 0.12852 -0.102491 0.359736 0.569248 -0.571192 -0.277686 -0.330084 -0.0465571 -0.222146 -0.398535 -0.252526 0.0626542 -0.0919317 0.242982 -0.284985 0.102297 -0.484199 -0.0634281 0.585745 0.154998 0.32386 0.203739 0.146867 0.170396 0.101049]\n",
      ", name conv2d_0.b_0, dtype: VarType.FP32 shape: [20] \tlod: {}\n",
      "\tdim: 20\n",
      "\tlayout: NCHW\n",
      "\tdtype: float\n",
      "\tdata: [-0.0026732 -0.0119315 -0.0122265 0.0223614 -0.0268466 -0.0362108 -0.00777895 0.00256775 -0.0444873 -0.0125939 -0.00921366 0.0233525 -0.00125153 0.0248584 -0.0371166 -0.00752332 -0.0391954 0.0053727 -0.0283431 0.00220547]\n",
      ", name conv2d_1.w_0, dtype: VarType.FP32 shape: [20, 20, 5, 5] \tlod: {}\n",
      "\tdim: 20, 20, 5, 5\n",
      "\tlayout: NCHW\n",
      "\tdtype: float\n",
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      ", name conv2d_1.b_0, dtype: VarType.FP32 shape: [20] \tlod: {}\n",
      "\tdim: 20\n",
      "\tlayout: NCHW\n",
      "\tdtype: float\n",
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-0.00414701 0.0614334 0.0582879 -0.00615617 -0.056068 -0.130936 0.0507278 0.0227186 0.0399874 -0.0317817 -0.0330636 0.0627173 -0.0268653 0.0625184 0.0288765 0.0840175 0.0681955 0.021153 -0.0444836 0.052257 0.10763 0.0451001 0.105572 -0.0580414 0.0317749]\n",
      ", name linear_0.b_0, dtype: VarType.FP32 shape: [1] \tlod: {}\n",
      "\tdim: 1\n",
      "\tlayout: NCHW\n",
      "\tdtype: float\n",
      "\tdata: [0.0525604]\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "# 查看保存的模型的属性\n",
    "print(model.parameters())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 测试模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读入一张图片进行测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Cache file /root/.cache/paddle/dataset/mnist/t10k-images-idx3-ubyte.gz not found, downloading https://dataset.bj.bcebos.com/mnist/t10k-images-idx3-ubyte.gz \n",
      "Begin to download\n",
      "\n",
      "Download finished\n",
      "Cache file /root/.cache/paddle/dataset/mnist/t10k-labels-idx1-ubyte.gz not found, downloading https://dataset.bj.bcebos.com/mnist/t10k-labels-idx1-ubyte.gz \n",
      "Begin to download\n",
      "..\n",
      "Download finished\n"
     ]
    }
   ],
   "source": [
    "# 读入测试集，并将测试集也乱序\n",
    "testset = paddle.dataset.mnist.test()\n",
    "test_reader = paddle.batch(testset, batch_size=100)\n",
    "test_reader = fluid.io.shuffle(test_reader, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "图像数据形状和对应数据为: (100, 1, 28, 28)\n",
      "图像标签形状和对应数据为: (100, 1) [8.]\n",
      "[8.]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 以迭代的形式读取数据\n",
    "for batch_id, data in enumerate(test_reader()):\n",
    "    # 获得图像数据，并转为float32类型的数组，遍历list，将每个元素从728转换为[1,28,28]，最后将list转换为array\n",
    "    img_data = np.array([np.reshape(np.array(x[0]).astype('float32'), [1, 28, 28]) for x in data])\n",
    "    # 获得图像标签数据，并转为float32类型的数组，将list转换为array并在后面添加一个维度\n",
    "    label_data = np.array([x[1] for x in data]).astype('float32').reshape(-1, 1)\n",
    "    break\n",
    "# 打印数据形状\n",
    "print(\"图像数据形状和对应数据为:\", img_data.shape)\n",
    "print(\"图像标签形状和对应数据为:\", label_data.shape, label_data[0])\n",
    "# 显示第一batch的第一个图像\n",
    "img = img_data[0][0]\n",
    "label = label_data[0]\n",
    "print(label)\n",
    "plt.figure(\"Image\")  # 图像窗口名称\n",
    "plt.imshow(img)\n",
    "plt.axis('on')  # 关掉坐标轴为 off\n",
    "plt.title('image')  # 图像题目\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 1, 28, 28)\n",
      "本次预测是数字是： [[8]]\n"
     ]
    }
   ],
   "source": [
    "with fluid.dygraph.guard():\n",
    "    model = MNIST()\n",
    "    params_file_path = 'mnist'\n",
    "    # 加载模型参数\n",
    "    model_dict, _ = fluid.load_dygraph('mnist')\n",
    "    model.load_dict(model_dict)\n",
    "    # 灌入数据进行测试\n",
    "    model.eval()\n",
    "    img = img.reshape(1, 1, 28, 28)  # 将图片数据维度调整为[1,1,28,28]\n",
    "    print(img.shape)\n",
    "    result = model(fluid.dygraph.to_variable(img))\n",
    "    print('本次预测是数字是：', result.numpy().astype('int32'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "需要注意的是，只有多层卷积神经网络识别的准确率高，单层全连接网络和多层全连接网络效果都不怎么样"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读入100张图片，测试模型准确率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 读入测试集，并将测试集也乱序\n",
    "testset = paddle.dataset.mnist.test()\n",
    "test_reader = paddle.batch(testset, batch_size=100)\n",
    "test_reader = fluid.io.shuffle(test_reader, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start evaluation .......\n",
      "loss=1.1355495101213455\n"
     ]
    }
   ],
   "source": [
    "with fluid.dygraph.guard():\n",
    "    print('start evaluation .......')\n",
    "    # 加载模型参数\n",
    "    model = MNIST()\n",
    "    model_state_dict, _ = fluid.load_dygraph('mnist')\n",
    "    model.load_dict(model_state_dict)\n",
    "\n",
    "    model.eval()\n",
    "\n",
    "    avg_loss_set = []\n",
    "#     avg_acc_set = []\n",
    "    for batch_id, data in enumerate(test_reader()):\n",
    "        # 获得图像数据，并转为float32类型的数组，遍历list，将每个元素从728转换为[1,28,28]，最后将list转换为array\n",
    "        img_data = np.array([np.reshape(np.array(x[0]).astype('float32'), [1, 28, 28]) for x in data])\n",
    "        # 获得图像标签数据，并转为float32类型的数组，将list转换为array并在后面添加一个维度\n",
    "        label_data = np.array([x[1] for x in data]).astype('float32').reshape(-1, 1)\n",
    "        \n",
    "        img = fluid.dygraph.to_variable(img_data)\n",
    "        label = fluid.dygraph.to_variable(label_data)\n",
    "        # print(img.shape,label.shape)\n",
    "        prediction = model(img)\n",
    "        loss = fluid.layers.square_error_cost(prediction, label)\n",
    "#         print(type(prediction),type(label),type(loss))\n",
    "#         print(prediction, fluid.dygraph.to_variable(label_data.astype('int64')))\n",
    "#         acc = fluid.layers.accuracy(input=prediction, label=fluid.dygraph.to_variable(label_data.astype('int64')))\n",
    "        avg_loss = fluid.layers.mean(loss)\n",
    "        avg_loss_set.append(float(avg_loss.numpy()))\n",
    "#         avg_acc = fluid.layers.mean(acc)\n",
    "#         avg_acc_set.append(float(avg_acc.numpy()))\n",
    "\n",
    "    # 计算多个batch的平均损失和准确率\n",
    "    avg_loss_val_mean = np.array(avg_loss_set).mean()\n",
    "#     avg_acc_val_mean = np.array(avg_acc_set).mean()\n",
    "\n",
    "#     print('loss={},acc={}'.format(avg_loss_val_mean, avg_acc_val_mean))\n",
    "    print('loss={}'.format(avg_loss_val_mean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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